On using human nonmonotonic reasoning to inform artificial systems

Abstract

People seem adept at drawing tentative conclusions when premises do not lead to a necessary conclusion. In contrast, the artificial nonmonotonic reasoning systems that have been developed are complex and do not function with ease. This apparent difference between human and artificial computational reasoning is sometimes considered puzzling and frustrating -if people can do it so easily, why can't we get computers to do it easily? The present paper explores the ways in which people attempt to solve nonmonotonic problems which contain conflict and shows that people do not in fact reason about these nonmonotonic problems so easily; they jump to conclusions easily, but they do not reason so well. However, some people do manage to sometimes reason quite well, in that their reasoning is based on ideas that are (classically) logically justifiable. This paper explores differences between these reasoners and others who cope less well. It also explores how the identification of the way in which these people reason can be used to inform artificial nonmonotonic reasoning systems.

Competence, reflective equilibrium, and dual-system theories

“…However, psychological data can inspire formal theories as well. Ford (2005), for example, investigates how experimental data informs artificial intelligence systems and has developed a formal system of nonmonotonic reasoning, which is inspired by psychological data (Ford 2004). Moreover, not only psychological data arbitrate between formal theories: the converse holds as well.…”

Reasoning from inconsistency: a field exploration

“…However, Ford and Billington believe that under the right circumstances humans can reason correctly nonmonotonically (e.g., graphical presentations might help). For more on the (absent) ability of human agents to reason nonmonotonically, see Ford (2005) further in this issue. Pelletier and Elio (in press; see also Elio & Pelletier, 1993) tested Lifschitz' basic default reasoning benchmark problems and inheritance benchmark problems.…”

Systematic rationality norms provide research roadmaps and clarity

“…However, psychological data can inspire formal theories as well. Ford (2005), for example, investigates how experimental data informs artificial intelligence systems and has developed a formal system of nonmonotonic reasoning, which is inspired by psychological data (Ford 2004). Moreover, not only psychological data arbitrate between formal theories: the converse holds as well.…”